用生理特征序列预测无创连续血压，精度达医疗级标准。
A Multidimensional Data-Driven Hybrid Transformer Framework for Non-invasive Continuous Blood Pressure Prediction
- 将心电/光电容积脉搏波特征转化为时序数据，融合多源信息建模
- 对收缩压和舒张压的误差均低于5.95 mmHg，95%一致性界限在±13.25内
- 适合可穿戴设备连续血压监测，为临床非侵入式测量提供新思路
目标：利用时序生理与人口统计特征，开发并评估一种无袖带连续血压（BP）估测方法。提出一种混合Transformer框架，从心电图/光电容积脉搏波衍生的特征序列中估计舒张压（DBP）和收缩压（SBP）。不直接处理原始波形，而是建模六种生理描述符与两种人口统计协变量的10步时序序列。多源时序编码模块结合Transformer、Kolmogorov-Arnold网络与XGBoost分支，捕捉互补的时序、非线性与表格信息。动态条件融合解码器采用差分多头注意力、令牌加权聚合与门控残差修正。采用联合优化目标同时训练DBP与SBP。基于MIMIC-III波形与临床数据库，共包含28,486段波形片段（来自203名受试者），特征生成保留53,621个观测值（来自166名受试者）。在2,431个独立测试窗口上，舒张压平均误差±标准差为0.41±3.74 mmHg，收缩压为-1.60±5.95 mmHg，95%一致性界限分别为[-6.93, 7.74]和[-13.25, 10.06] mmHg；误差在10 mmHg内的比例分别为98.48%和94.36%。该框架在本地重训练基线中实现了最低标准差与最窄一致性界限。意义：该特征序列融合框架提升了与参考血压的一致性，达到AAMI与BHS Grade A标准。本研究为回顾性分析，未进行正式设备验证；需进行受试者无关与外部验证后方可用于临床。
原文摘要 · Abstract (English)
Objective. To develop and evaluate a cuffless continuous blood pressure (BP) estimator using temporal physiological and demographic features. We propose a hybrid Transformer framework to estimate diastolic and systolic BP from ECG/PPG-derived feature sequences. Approach. Rather than raw waveforms, the framework models 10-step sequences of six physiological descriptors and two demographic covariates. A Multi-Source Temporal Encoder Module combines Transformer, Kolmogorov-Arnold Network, and XGBoost branches to capture complementary temporal, nonlinear, and tabular information. A Dynamic Conditional Fusion-Decoder applies differential multi-head attention, token-weighted aggregation, and gated residual correction. A robust composite objective jointly optimizes DBP and SBP. Main results. Using the MIMIC-III Waveform and Clinical Databases, the source pool comprised 28,486 waveform segments from 203 subjects, and feature generation retained 53,621 observations from 166 subjects. On 2,431 segment-level held-out test windows, mean error +/- standard deviation was 0.41 +/- 3.74 mmHg for diastolic BP and -1.60 +/- 5.95 mmHg for systolic BP, with 95% limits of agreement of [-6.93, 7.74] and [-13.25, 10.06] mmHg, respectively. The proportions within 10 mmHg were 98.48% and 94.36%. The framework achieved the lowest standard deviations and narrowest limits of agreement among the locally retrained baselines. Significance. The feature-sequence fusion framework improved agreement with reference BP and fell within numerical AAMI and BHS Grade A thresholds on this split. This retrospective analysis is not formal device validation; subject-disjoint and external evaluation remain necessary before clinical use.
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